• Soyoung Kim, Jae-Hoon Lee, Eun Jung Park, Hye Sun Lee, Seung Hyuk Baik, Tae Joo Jeon, Kang Young Lee, Young Hoon Ryu, and Jeonghyun Kang.
• Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
• Yonsei Med. J. 2023 May 1; 64 (5): 320326320-326.

PurposeWe investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients.Materials And MethodsAltogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters.ResultsThe incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015).ConclusionOur predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.© Copyright: Yonsei University College of Medicine 2023.

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